code: def main(cfg) -> None:
      logging.info(
          "\n\n************** Experiment configuration ***********"
      )
      logging.info(f"\n{OmegaConf.to_yaml(cfg)}")
  
      megatron_amp_O2 = cfg.model.get("megatron_amp_O2", False)
      plugins = []
      strategy = NLPDDPStrategy(
          no_ddp_communication_hook=True if megatron_amp_O2 else False,
          gradient_as_bucket_view=cfg.model.gradient_as_bucket_view,
          find_unused_parameters=False,
      )
  
      if cfg.trainer.precision in [
          16,
          "16",
          "bf16",
          "16-mixed",
          "bf16-mixed",
      ]:
          scaler = None
          if cfg.trainer.precision in [16, "16", "16-mixed"]:
              scaler = GradScaler(
                  init_scale=cfg.model.get(
                      "native_amp_init_scale", 2**32
                  ),
                  growth_interval=cfg.model.get(
                      "native_amp_growth_interval", 1000
                  ),
                  hysteresis=cfg.model.get("hysteresis", 2),
              )
              plugin_precision = "16-mixed"
          else:
              plugin_precision = "bf16-mixed"
          if megatron_amp_O2:
              plugins.append(
                  MegatronHalfPrecisionPlugin(
                      precision=plugin_precision,
                      device="cuda",
                      scaler=scaler,
                  )
              )
          else:
              plugins.append(
                  MixedPrecisionPlugin(
                      precision=plugin_precision,
                      device="cuda",
                      scaler=scaler,
                  )
              )
  
      if cfg.get("cluster_type", None) == "BCP":
          plugins.append(TorchElasticEnvironment())
  
      trainer = Trainer(
          plugins=plugins,
          strategy=strategy,
          **cfg.trainer,
          callbacks=[CustomProgressBar()],
      )
  
      # hydra interpolation does not work here as the interpolation key is lost when PTL saves hparams
      with open_dict(cfg):
          cfg.base_model.precision = cfg.trainer.precision
          cfg.delta_model.precision = cfg.trainer.precision
  
      base_model = MegatronRetrievalModel(cfg.base_model, trainer)
      delta_model = MegatronRetrievalModel(cfg.delta_model, trainer)
      make_base_shapes(
          base_model, delta_model, savefile=cfg.model.shape_file
      )
  
file_path: NeMo/examples/nlp/language_modeling/megatron_retro_cal_shape.py
imports: from omegaconf.omegaconf import OmegaConf, open_dict
  from pytorch_lightning import Trainer
  from pytorch_lightning.plugins.environments import TorchElasticEnvironment
  from pytorch_lightning.plugins.precision import MixedPrecisionPlugin
  from nemo.collections.nlp.models.language_modeling.megatron_retrieval_model import MegatronRetrievalModel
  from nemo.collections.nlp.modules.common.megatron.mup.shape import make_base_shapes
  from nemo.collections.nlp.parts.nlp_overrides import CustomProgressBar, GradScaler, MegatronHalfPrecisionPlugin, NLPDDPStrategy
  from nemo.core.config import hydra_runner
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: main(cfg) without return types